Cross-component adaptive loop filter

ABSTRACT

A method, computer program, and computer system is provided for video coding. Video data comprising a chroma component and a luma component is received. Luma samples are extracted from the luma component of the received video data. The chroma component is filtered by a cross-component adaptive loop filter (CC-ALF) based on a location of a chroma sample associated with the chroma component, the extracted luma samples, filter weights associated with the extracted luma samples, and an offset value.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority based on U.S. Provisional ApplicationNo. 62/904,644 (filed Sep. 23, 2019), the entirety of which isincorporated herein.

FIELD

This disclosure relates generally to field of data processing, and moreparticularly to video coding.

BACKGROUND

ITU-T VCEG (Q6/16) and ISO/IEC MPEG (JTC 1/SC 29/WG 11) published theH.265/HEVC (High Efficiency Video Coding) standard in 2013 (version 1)2014 (version 2) 2015 (version 3) and 2016 (version 4). In 2015, thesetwo standard organizations jointly formed the JVET (Joint VideoExploration Team) to explore the potential of developing the next videocoding standard beyond HEVC In October 2017, they issued the Joint Callfor Proposals on Video Compression with Capability beyond HEVC (CfP). ByFebruary 15, 2018, a total of 22 CfP responses on standard dynamic range(SDR), 12 CfP responses on high dynamic range (HDR), and 12 CfPresponses on 360 video categories were submitted, respectively. In April2018, all received CfP responses were evaluated in the 122 MPEG/10thJVET meeting. As a result of this meeting, JVET formally launched thestandardization process of next-generation video coding beyond HEVC. Thenew standard was named Versatile Video Coding (VVC), and JVET wasrenamed as Joint Video Expert Team.

SUMMARY

Embodiments relate to a method, system, and computer readable medium forvideo coding. According to one aspect, a method for video coding isprovided. The method may include receiving video data comprising achroma component and a luma component. Luma samples are extracted fromthe luma component of the received video data. The chroma component isfiltered by a cross-component adaptive loop filter (CC-ALF) based on alocation of a chroma sample associated with the chroma component, theextracted luma samples, filter weights associated with the extractedluma samples, and an offset value.

According to another aspect, a computer system for video coding isprovided. The computer system may include one or more processors, one ormore computer-readable memories, one or more computer-readable tangiblestorage devices, and program instructions stored on at least one of theone or more storage devices for execution by at least one of the one ormore processors via at least one of the one or more memories, wherebythe computer system is capable of performing a method. The method mayinclude receiving video data comprising a chroma component and a lumacomponent. Luma samples are extracted from the luma component of thereceived video data. The chroma component is filtered by across-component adaptive loop filter (CC-ALF) based on a location of achroma sample associated with the chroma component, the extracted lumasamples, filter weights associated with the extracted luma samples, andan offset value.

According to yet another aspect, a computer readable medium for videocoding is provided. The computer readable medium may include one or morecomputer-readable storage devices and program instructions stored on atleast one of the one or more tangible storage devices, the programinstructions executable by a processor. The program instructions areexecutable by a processor for performing a method that may accordinglyinclude receiving video data comprising a chroma component and a lumacomponent. Luma samples are extracted from the luma component of thereceived video data. The chroma component is filtered by across-component adaptive loop filter (CC-ALF) based on a location of achroma sample associated with the chroma component, the extracted lumasamples, filter weights associated with the extracted luma samples, andan offset value.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages will become apparentfrom the following detailed description of illustrative embodiments,which is to be read in connection with the accompanying drawings. Thevarious features of the drawings are not to scale as the illustrationsare for clarity in facilitating the understanding of one skilled in theart in conjunction with the detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2 is a block diagram of a cross-component adaptive loop filter(CC-ALF) for video coding, according to at least one embodiment;

FIG. 3 is an operational flowchart illustrating the steps carried out bya program for video coding, according to at least one embodiment;

FIG. 4 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 5 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, according to at leastone embodiment; and

FIG. 6 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 5, according to at least one embodiment.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. Those structures and methods may, however, beembodied in many different forms and should not be construed as limitedto the exemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope to those skilled in the art. Inthe description, details of well-known features and techniques may beomitted to avoid unnecessarily obscuring the presented embodiments.

Embodiments relate generally to the field of data processing, and moreparticularly to media processing. The following described exemplaryembodiments provide a system, method and computer program to, amongother things, refine a chroma component of video data using a lumacomponent of the video data. Therefore, some embodiments have thecapacity to improve the field of computing by allowing for improvedencoding and decoding through filtering of luma samples used forencoding and decoding.

As previously described, ITU-T VCEG (Q6/16) and ISO/IEC MPEG (JTC 1/SC29/WG 11) published the H.265/HEVC (High Efficiency Video Coding)standard in 2013 (version 1) 2014 (version 2) 2015 (version 3) and 2016(version 4). In 2015, these two standard organizations jointly formedthe JVET (Joint Video Exploration Team) to explore the potential ofdeveloping the next video coding standard beyond HEVC In October 2017,they issued the Joint Call for Proposals on Video Compression withCapability beyond HEVC (CfP). By Feb. 15, 2018, a total of 22 CfPresponses on standard dynamic range (SDR), 12 CfP responses on highdynamic range (HDR), and 12 CfP responses on 360 video categories weresubmitted, respectively. In April 2018, all received CfP responses wereevaluated in the 122 MPEG/10th JVET meeting. As a result of thismeeting, JVET formally launched the standardization process ofnext-generation video coding beyond HEVC. The new standard was namedVersatile Video Coding (VVC), and JVET was renamed as Joint Video ExpertTeam. In VVC, a cross-component adaptive loop filter (CC-ALF) may makeuse of luma sample values from video data to refine a chroma componentof the video data. However, when neighboring luma samples are toodifferent from the current luma sample value, it may cause negativefiltering effect toward the chroma sample to be filtered. It may beadvantageous, therefore, to filter the luma samples to prevent thenegative filtering effects.

Aspects are described herein with reference to flowchart illustrationsand/or block diagrams of methods, apparatus (systems), and computerreadable media according to the various embodiments. It will beunderstood that each block of the flowchart illustrations and/or blockdiagrams, and combinations of blocks in the flowchart illustrationsand/or block diagrams, can be implemented by computer readable programinstructions.

The following described exemplary embodiments provide a system, methodand computer program that allows for refinement of a chroma component ofvideo data using a luma component of the video data. Referring now toFIG. 1, a functional block diagram of a networked computer environmentillustrating a media processing system 100 (hereinafter “system”) forvideo coding. It should be appreciated that FIG. 1 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may be madebased on design and implementation requirements.

The system 100 may include a computer 102 and a server computer 114. Thecomputer 102 may communicate with the server computer 114 via acommunication network 110 (hereinafter “network”). The computer 102 mayinclude a processor 104 and a software program 108 that is stored on adata storage device 106 and is enabled to interface with a user andcommunicate with the server computer 114. As will be discussed belowwith reference to FIG. 4 the computer 102 may include internalcomponents 800A and external components 900A, respectively, and theserver computer 114 may include internal components 800B and externalcomponents 900B, respectively. The computer 102 may be, for example, amobile device, a telephone, a personal digital assistant, a netbook, alaptop computer, a tablet computer, a desktop computer, or any type ofcomputing devices capable of running a program, accessing a network, andaccessing a database.

The server computer 114 may also operate in a cloud computing servicemodel, such as Software as a Service (SaaS), Platform as a Service(PaaS), or Infrastructure as a Service (IaaS), as discussed below withrespect to FIGS. 5 and 6. The server computer 114 may also be located ina cloud computing deployment model, such as a private cloud, communitycloud, public cloud, or hybrid cloud.

The server computer 114, which may be used for video coding is enabledto run an CC-ALF Program 116 (hereinafter “program”) that may interactwith a database 112. The CC-ALF Program method is explained in moredetail below with respect to FIG. 3. In one embodiment, the computer 102may operate as an input device including a user interface while theprogram 116 may run primarily on server computer 114. In an alternativeembodiment, the program 116 may run primarily on one or more computers102 while the server computer 114 may be used for processing and storageof data used by the program 116. It should be noted that the program 116may be a standalone program or may be integrated into a larger CC-ALFprogram.

It should be noted, however, that processing for the program 116 may, insome instances be shared amongst the computers 102 and the servercomputers 114 in any ratio. In another embodiment, the program 116 mayoperate on more than one computer, server computer, or some combinationof computers and server computers, for example, a plurality of computers102 communicating across the network 110 with a single server computer114. In another embodiment, for example, the program 116 may operate ona plurality of server computers 114 communicating across the network 110with a plurality of client computers. Alternatively, the program mayoperate on a network server communicating across the network with aserver and a plurality of client computers.

The network 110 may include wired connections, wireless connections,fiber optic connections, or some combination thereof. In general, thenetwork 110 can be any combination of connections and protocols thatwill support communications between the computer 102 and the servercomputer 114. The network 110 may include various types of networks,such as, for example, a local area network (LAN), a wide area network(WAN) such as the Internet, a telecommunication network such as thePublic Switched Telephone Network (PSTN), a wireless network, a publicswitched network, a satellite network, a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a metropolitan area network(MAN), a private network, an ad hoc network, an intranet, a fiberoptic-based network, or the like, and/or a combination of these or othertypes of networks.

The number and arrangement of devices and networks shown in FIG. 1 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 1. Furthermore, two or more devices shown in FIG. 1 may beimplemented within a single device, or a single device shown in FIG. 1may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) of system100 may perform one or more functions described as being performed byanother set of devices of system 100.

Referring now to FIG. 2, a block diagram of an exemplary cross-componentadaptive loop filter (CC-ALF filter) 200 with block-based filteradaption is depicted. The CC-ALF filter 200 may include a chromacomponent 202 and a luma component 204. For the luma component 204, onefilter among twenty-five filters may be selected for each 4×4 block,based on the direction and activity of local gradients. Two diamondfilter shapes may be used. A 7-by-7 diamond shape 202 may be applied forluma components and a 5-by-5 diamond shape may be applied for chromacomponents.

The CC-ALF filter 200 may make use of luma sample values to refine eachchroma component by applying a linear, diamond shaped filter to the lumachannel for each chroma component. The filter coefficients aretransmitted may be scaled by a factor of 2¹⁰ and rounded for fixed pointrepresentation. The application of the filters may be controlled on avariable block size and signalled by a context-coded flag received foreach block of samples. The block size along with an CC-ALF enabling flagmay be received at the slice-level for each chroma component.

For the chroma components in a picture, a single set of ALF coefficientsC0-C6 may is applied for the chroma component 202.

For the luma component 204, each 4-by-4 block may be categorized intoone of twenty-five classes. A classification index C may be derivedbased on its directionality D and a quantized value of activity Â, suchthat C=5D+Â. To calculate D and Â, gradients of the horizontal, verticaland two diagonal direction may be calculated using 1-D Laplacian:

${g_{v} = {\sum\limits_{k = {i - 2}}^{i + 3}{\sum\limits_{l = {j - 2}}^{j + 3}V_{k,l}}}},{V_{k,l} = {{{2{R\left( {k,l} \right)}} - {R\left( {k,{l - 1}} \right)} - {R\left( {k,{l + 1}} \right)}}}}$${g_{h} = {\sum\limits_{k = {i - 2}}^{i + 3}{\sum\limits_{l = {j - 2}}^{j + 3}H_{k,l}}}},{H_{k,l} = {{{2{R\left( {k,l} \right)}} - {R\left( {{k - 1},l} \right)} - {R\left( {{k + 1},l} \right)}}}}$${g_{d1} = {\sum\limits_{k = {i - 2}}^{i + 3}{\sum\limits_{l = {j - 3}}^{j + 3}{D1_{k,l}}}}},{{D\; 1_{k,l}} = {{{2{R\left( {k,l} \right)}} - {R\left( {{k - 1},{l - 1}} \right)} - {R\left( {{k + 1},{l + 1}} \right)}}}}$${g_{d2} = {\sum\limits_{k = {i - 2}}^{i + 3}{\sum\limits_{j = {j - 2}}^{j + 3}{D2_{k,l}}}}},{{D\; 2_{k,l}} = {{{2{R\left( {k,l} \right)}} - {R\left( {{k - 1},{l + 1}} \right)} - {R\left( {{k + 1},{l - 1}} \right)}}}},$

where indices i and j may refer to the coordinates of the upper leftsample within the 4×4 block and R(I, j) may indicate a reconstructedsample at coordinate (I, j). To reduce the complexity of blockclassification, the subsampled 1-D Laplacian calculation is applied. Thesame subsampled positions may be used for gradient calculation of alldirections.

Maximum and minimum D values of the gradients of horizontal and verticaldirections may be set as:

-   g_(h,v) ^(max)=max(g_(h), g_(v)), g_(h,v) ^(min)=min(g_(h), g_(v)).

The maximum and minimum values of the gradient of two diagonaldirections may be set as:

-   g_(d0,d1) ^(max)=max(g_(d0), g_(d1)), g_(d0,d1) ^(min)=min(g_(d0),    g_(d1))

To derive the value of the directionality D, the maximum and minimumvalues may be compared against each other and with two thresholds t₁ andt₂. If both g_(h,v) ^(max)≤t₁·g_(h,v) ^(min) and g_(d0,d1)^(max)≤t₁·g_(d0,d1) ^(min) are true, D is set to 0. If g_(h,v)^(max)/g_(h,v) ^(min)>g_(d0,d1) ^(max)/g_(d0,d1) ^(min), then if g_(h,v)^(max)>t₂·g_(h,v) ^(min), D may be set to 2; otherwise D may be setto 1. If g_(h,v) ^(max)/g_(h,v) ^(min)≤g_(d0,d1) ^(max)/g_(d0,d1)^(min), then if g_(d0,d1) ^(max)>t₂·g_(d0,d1) ^(min), D may be set to 4;otherwise D may be set to 3.

The activity value may be calculated as:

${A = {\sum\limits_{k = {i - 2}}^{i + 3}{\sum\limits_{l = {j - 2}}^{j + 3}\left( {V_{k,l} + H_{k,l}} \right)}}}.$

A may be further quantized to the range of 0 to 4, inclusively. Thequantized value may be denoted as Â.

At a decoder side, when ALF may be enabled, each sample R(i, j) withinthe CU may be filtered, resulting in sample value

${R^{\prime}\left( {i,j} \right)} = {{R\left( {i,j} \right)} + \left( {\left( {{\sum\limits_{k \neq 0}{\sum\limits_{l \neq 0}{{f\left( {k,l} \right)} \times {K\left( {{{R\left( {{i + k},{j + l}} \right)} - {R\left( {i,j} \right)}},{c\left( {k,l} \right)}} \right)}}}} + 64} \right)7} \right)}$

where f(k, l) may denote the decoded filter coefficients, K(x, y) may bea clipping function and c(k, l) may denote decoded clipping parameters.The variables k and 1 may vary between

${{- \frac{L}{2}}\mspace{14mu} {and}\mspace{14mu} \frac{L}{2}},$

where L may denote the filter length. The clipping function K(x,y)=min(y, max(−y, x)) may correspond to the function Clip3 (−y, y, x).By incorporating this clipping function, the loop filtering methodbecomes a non-linear process, known as Non-Linear ALF.

According to one or more embodiments, the CC-ALF filtering process maybe denoted by O (x, y)=Σw(i, j)I_(L)(x+i,y+j) and I′_(C)(x, y)=I_(C)(x,y)+O(x, y), where (x, y) may indicate a location of a chroma sample tobe filtered, I_(L)(x+i, y+j) may be input luma samples, w(i, j) maydenote filter weights associated with each input sample, O(x, y) may bean offset computed by luma channel, and I_(C)(x, y) and I′_(C)(x, y) maybe the chroma samples before and after applying CC-ALF filtering,respectively.

According to one or more embodiments, a non-linear filter in CC-ALF maybe applied to constrain the strength of filtering in CC-ALF. Thefiltering process may be denoted by O(x, y)=Σw(i, j) f (I_(L)(x+i, y+j),I_(L)(x, y), c(i, j)), where f(*) may be a non-linear function takingthe input luma sample I_(L)(x+i, y+j) located at (x+i, y+j), and currentluma sample I_(L)(x, y) as inputs. In one or more embodiments, f (a, b,δ)=min(b+c, max(a, b−δ)) may be the clipping function, and c(i, j) maybe clipping values applied on the samples located at (x+i, y+j). In oneor more embodiments, f (a, b, δ) may be a high-pass filter such as:

${f\left( {a,b,\delta} \right)} = \left\{ {\begin{matrix}0 & {{{if}\mspace{14mu} a} < {b - {\delta \mspace{14mu} {or}\mspace{14mu} a}} > {b + \delta}} \\a & {otherwise}\end{matrix}.} \right.$

In one or more embodiments, f (a, b, δ₁, δ₂) may be a band-pass filtersuch as:

${f\left( {a,b,\delta_{1},\delta_{2}} \right)} = \left\{ {\begin{matrix}a & {{{{if}\mspace{14mu} b} - \delta_{1}} < a < {b - {\delta_{2}\mspace{14mu} {or}\mspace{14mu} b} + \delta_{2}} < a < {b + \delta_{1}}} \\0 & {otherwise}\end{matrix}.} \right.$

The clipping value δ(i, j) may be signaled in high level syntax,including but not limited to Slice Header, PPS (Picture Parameter Set),SPS (Sequence Parameter Set), and/or APS. The clipping value δ(i, j) maybe derived directly. For example, it may be derived based on thedifference between current sample and its neighboring samples. Theclipping value δ(i, j) may alternatively be derived based on previouslycoded information, including, but not limited to reconstructed values,coding mode, block partitioning size.

According to one or more embodiments, the CC-ALF filtering process maybe denoted by O(x, y)=Σf (I_(L)(x+i, y+j), I_(L)(x, y))I_(L)(x+i, y+j),where f (I_(L)(x+i, y+j), I_(L)(x, y)) may derive filter weights. In oneor more embodiments, the filter weights may be derived based on thedifference between current sample and its neighboring samples. In one ormore embodiments, the difference between the current sample and itsneighboring samples may be d=I_(L)(x+i, y+j)−I_(L)(x, y). f (I_(L)(x+i,y+j), I_(L)(x, y)) may a Gaussian function of d, such that

${{f(d)} = e^{({- \frac{d^{2}}{2\sigma^{2}}})}},$

where σ may be a given controlling parameter specifying the filteringstrength. In one or more embodiments, the outputs of function f(*) fordifferent input may be pre-defined. For example, the pre-defined outputvalues may be be specified in HLS or stored as a constant LUT.

According to one or more embodiments, instead of using the luma samplesfiltered by sample-adaptive offset (SAO) as inputs as used in theoriginal CC-ALF design, a different input source of CC-ALF may be used.The output of CC-ALF may be applied on Cb/Cr after chroma ALF filtering.In one or more embodiments, the input luma samples of CC-ALF may be besamples before luma SAO filtering and after luma deblocking filtering.In one or more embodiments, the input of CC-ALF may be reconstructedluma samples before applying luma deblocking filtering. In one or moreembodiments, the input of CC-ALF may be luma samples after applying lumaALF filtering.

According to one or more embodiments, instead of using luma samples asinput of CC-ALF and the output is applied on the reconstructed chromasamples, chroma samples may be used as input of CC-ALF and the outputmay be applied on the luma samples. Chroma samples can refer to eitherCb samples or Cr samples, or a combination of Cb and Cr samples. In oneor more embodiments, the input chroma samples of CC-ALF may be chromasamples before applying chroma ALF filtering and after chroma SAOfiltering. In one or more embodiments, the input chroma samples ofCC-ALF may be samples before chroma SAO filtering and after chromadeblocking filtering. In one or more embodiments, the input of CC-ALFmay be reconstructed chroma samples before applying chroma deblockingfiltering. In one or more embodiments, the input of CC-ALF may be chromasamples after applying chroma ALF filtering.

According to one or more embodiments, Cb samples may be used as input ofCC-ALF and the output may be applied on the Cr samples. Alternatively,Cr samples may be used as input of CC-ALF and the output may be appliedon the Cb samples. In one or more embodiments, the input Cb (or Cr)samples of CC-ALF may be Cb (or Cr) samples before applying Cb (or Cr)ALF filtering and after Cb (or Cr) SAO filtering. In one or moreembodiments, the input Cb (or Cr) samples of CC-ALF may be samplesbefore Cb (or Cr) SAO filtering and after Cb (or Cr) deblocking. In oneor more embodiments, the input of CC-ALF may be reconstructed lumasamples before applying luma deblocking filtering. In one or moreembodiments, the input of CC-ALF may be Cb (or Cr) samples afterapplying Cb (or Cr) ALF filtering.

According to one or more embodiments, instead of processing luma ALFfiltering before chroma ALF filtering, chroma ALF filtering may beprocessed before luma ALF filtering. The processing order of loopfiltering of different color components may be pre-defined.Alternatively, the processing order of loop filtering of different colorcomponents may be signaled by an individual flag or index.

Referring now to FIG. 3, an operational flowchart 300 illustrating thesteps carried out by a program for video coding is depicted. FIG. 3 maybe described with the aid of FIGS. 1 and 2. As previously described, theCC-ALF Program 116 (FIG. 1) may constrain CC-ALF coefficients.

At 302, video data comprising a chroma component and a luma component isreceived. The video data may include multiple frames that may eachinclude a chroma component and a luma component. In operation, theCC-ALF Program 116 (FIG. 1) on the server computer 114 (FIG. 1) mayreceive video data from the computer 102 (FIG. 1) over the communicationnetwork 110 (FIG. 1) or may retrieve the video data 204 from thedatabase 112 (FIG. 1).

At 304, luma samples are extracted from the luma component of thereceived video data. The luma samples may be four pixels by four pixelin size. The luma samples may be used to refine the chroma component. Inoperation, the CC-ALF Program 116 (FIG. 1) may extract luma samples fromthe received video data.

At 306, the chroma component is filtered by a cross-component adaptiveloop filter (CC-ALF) based on a location of a chroma sample associatedwith the chroma component, the extracted luma samples, filter weightsassociated with the extracted luma samples, and an offset value. Inoperation, the CC-ALF Program 116 (FIG. 1) use the coefficients C0-C6 ofthe chroma component 202 (FIG. 2) and the coefficients C0-C12 of theluma component 204 (FIG. 2) of the CC-ALF filter 200 (FIG. 2) based onthe luma samples to filter the chroma component of the received videodata. The CC-ALF Program 116 may encode and/or decode video data basedon the filtered chroma component

It may be appreciated that FIG. 3 provides only an illustration of oneimplementation and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

FIG. 4 is a block diagram 400 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment. It should be appreciated that FIG. 4 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may be madebased on design and implementation requirements.

Computer 102 (FIG. 1) and server computer 114 (FIG. 1) may includerespective sets of internal components 800A,B and external components900A,B illustrated in FIG. 4. Each of the sets of internal components800 include one or more processors 820, one or more computer-readableRAMs 822 and one or more computer-readable ROMs 824 on one or more buses826, one or more operating systems 828, and one or morecomputer-readable tangible storage devices 830.

Processor 820 is implemented in hardware, firmware, or a combination ofhardware and software. Processor 820 is a central processing unit (CPU),a graphics processing unit (GPU), an accelerated processing unit (APU),a microprocessor, a microcontroller, a digital signal processor (DSP), afield-programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), or another type of processing component. In someimplementations, processor 820 includes one or more processors capableof being programmed to perform a function. Bus 826 includes a componentthat permits communication among the internal components 800A,B.

The one or more operating systems 828, the software program 108 (FIG. 1)and the CC-ALF Program 116 (FIG. 1) on server computer 114 (FIG. 1) arestored on one or more of the respective computer-readable tangiblestorage devices 830 for execution by one or more of the respectiveprocessors 820 via one or more of the respective RAMs 822 (whichtypically include cache memory). In the embodiment illustrated in FIG.4, each of the computer-readable tangible storage devices 830 is amagnetic disk storage device of an internal hard drive. Alternatively,each of the computer-readable tangible storage devices 830 is asemiconductor storage device such as ROM 824, EPROM, flash memory, anoptical disk, a magneto-optic disk, a solid state disk, a compact disc(CD), a digital versatile disc (DVD), a floppy disk, a cartridge, amagnetic tape, and/or another type of non-transitory computer-readabletangible storage device that can store a computer program and digitalinformation.

Each set of internal components 800A,B also includes a R/W drive orinterface 832 to read from and write to one or more portablecomputer-readable tangible storage devices 936 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 (FIG. 1) and the CC-ALF Program 116 (FIG. 1) can be storedon one or more of the respective portable computer-readable tangiblestorage devices 936, read via the respective R/W drive or interface 832and loaded into the respective hard drive 830.

Each set of internal components 800A,B also includes network adapters orinterfaces 836 such as a TCP/IP adapter cards; wireless Wi-Fi interfacecards; or 3G, 4G, or 5G wireless interface cards or other wired orwireless communication links. The software program 108 (FIG. 1) and theCC-ALF Program 116 (FIG. 1) on the server computer 114 (FIG. 1) can bedownloaded to the computer 102 (FIG. 1) and server computer 114 from anexternal computer via a network (for example, the Internet, a local areanetwork or other, wide area network) and respective network adapters orinterfaces 836. From the network adapters or interfaces 836, thesoftware program 108 and the CC-ALF Program 116 on the server computer114 are loaded into the respective hard drive 830. The network maycomprise copper wires, optical fibers, wireless transmission, routers,firewalls, switches, gateway computers and/or edge servers.

Each of the sets of external components 900A,B can include a computerdisplay monitor 920, a keyboard 930, and a computer mouse 934. Externalcomponents 900A,B can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 800A,B also includes device drivers 840to interface to computer display monitor 920, keyboard 930 and computermouse 934. The device drivers 840, R/W drive or interface 832 andnetwork adapter or interface 836 comprise hardware and software (storedin storage device 830 and/or ROM 824).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,some embodiments are capable of being implemented in conjunction withany other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring to FIG. 5, illustrative cloud computing environment 500 isdepicted. As shown, cloud computing environment 500 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Cloud computingnodes 10 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 500 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that cloud computingnodes 10 and cloud computing environment 500 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring to FIG. 6, a set of functional abstraction layers 600 providedby cloud computing environment 500 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments are notlimited thereto. As depicted, the following layers and correspondingfunctions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and Cross-Component Adaptive Loop Filtering(CC-ALF) 96. CC-ALF 96 may allow for encoding and decoding of video datawith constrained filtering coefficients to limit the dynamic range ofthe video data.

Some embodiments may relate to a system, a method, and/or a computerreadable medium at any possible technical detail level of integration.The computer readable medium may include a computer-readablenon-transitory storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outoperations.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program code/instructions for carrying out operationsmay be assembler instructions, instruction-set-architecture (ISA)instructions, machine instructions, machine dependent instructions,microcode, firmware instructions, state-setting data, configuration datafor integrated circuitry, or either source code or object code writtenin any combination of one or more programming languages, including anobject oriented programming language such as Smalltalk, C++, or thelike, and procedural programming languages, such as the “C” programminglanguage or similar programming languages. The computer readable programinstructions may execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects or operations.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer readable media according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of instructions,which comprises one or more executable instructions for implementing thespecified logical function(s). The method, computer system, and computerreadable medium may include additional blocks, fewer blocks, differentblocks, or differently arranged blocks than those depicted in theFigures. In some alternative implementations, the functions noted in theblocks may occur out of the order noted in the Figures. For example, twoblocks shown in succession may, in fact, be executed concurrently orsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

The descriptions of the various aspects and embodiments have beenpresented for purposes of illustration, but are not intended to beexhaustive or limited to the embodiments disclosed. Even thoughcombinations of features are recited in the claims and/or disclosed inthe specification, these combinations are not intended to limit thedisclosure of possible implementations. In fact, many of these featuresmay be combined in ways not specifically recited in the claims and/ordisclosed in the specification. Although each dependent claim listedbelow may directly depend on only one claim, the disclosure of possibleimplementations includes each dependent claim in combination with everyother claim in the claim set. Many modifications and variations will beapparent to those of ordinary skill in the art without departing fromthe scope of the described embodiments. The terminology used herein waschosen to best explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method of coding video data, executable by aprocessor, comprising: receiving video data comprising a chromacomponent and a luma component; extracting luma samples from the lumacomponent; and filtering the chroma component by a cross-componentadaptive loop filter (CC-ALF) based on a location of a chroma sampleassociated with the chroma component, the extracted luma samples, filterweights associated with the extracted luma samples, and an offset value.2. The method of claim 1, wherein a non-linear filter component isapplied to the CC-ALF to constrain a filter strength associated with theCC-ALF.
 3. The method of claim 2, wherein the non-linear filtercomponent comprises at least one from among a clipping function, ahigh-pass filter, and a band-pass filter.
 4. The method of claim 1,wherein the filter weights are derived based on a difference between acurrent luma sample from among the extracted luma samples and one ormore neighboring samples associated with the current luma sample.
 5. Themethod of claim 1, wherein a luma deblocking filter process is appliedto the extracted luma samples and a sample-adaptive offset filteringprocess has not been applied.
 6. The method of claim 1, wherein an inputof CC-ALF is a reconstructed luma sample to which luma deblockingfiltering has not been applied.
 7. The method of claim 1, wherein, oneor more chroma samples associated with the chroma component are used asan input to the CC-ALF and one or more luma samples are received as anoutput.
 8. The method of claim 7, wherein the one or more chroma samplesinclude at least one from among red-difference chroma samples,blue-difference chroma samples, and a combination of red-differencechroma samples and blue-difference chroma samples.
 9. The method ofclaim 1, wherein the CC-ALF processes the luma component prior toprocessing the chroma component.
 10. The method of claim 1, furthercomprising encoding or decoding the video data based on the filteredchroma component.
 11. A computer system for video coding, the computersystem comprising: one or more computer-readable non-transitory storagemedia configured to store computer program code; and one or morecomputer processors configured to access said computer program code andoperate as instructed by said computer program code, said computerprogram code including: receiving code configured to cause the one ormore computer processors to receive video data comprising a chromacomponent and a luma component; extracting code configured to cause theone or more computer processors to extract luma samples from the lumacomponent; and filtering code configured to cause the one or morecomputer processors to filter the chroma component by a cross-componentadaptive loop filter (CC-ALF) based on a location of a chroma sampleassociated with the chroma component, the extracted luma samples, filterweights associated with the extracted luma samples, and an offset value.12. The computer system of claim 11, wherein a non-linear filtercomponent is applied to the CC-ALF to constrain a filter strengthassociated with the CC-ALF.
 13. The computer system of claim 12, whereinthe non-linear filter component comprises at least one from among aclipping function, a high-pass filter, and a band-pass filter.
 14. Thecomputer system of claim 11, wherein the filter weights are derivedbased on a difference between a current luma sample from among theextracted luma samples and one or more neighboring samples associatedwith the current luma sample.
 15. The computer system of claim 11,wherein a luma deblocking filter process is applied to the extractedluma samples and a sample-adaptive offset filtering process has not beenapplied.
 16. The computer system of claim 11, wherein an input of CC-ALFis a reconstructed luma sample to which luma deblocking filtering hasnot been applied.
 17. The computer system of claim 11, wherein, one ormore chroma samples associated with the chroma component are used as aninput to the CC-ALF and one or more luma samples are received as anoutput.
 18. The computer system of claim 17, wherein the one or morechroma samples include at least one from among red-difference chromasamples, blue-difference chroma samples, and a combination ofred-difference chroma samples and blue-difference chroma samples. 19.The computer system of claim 11, further comprising encoding anddecoding code configured to cause the one or more computer processors toencode or decode the video data based on the filtered chroma component.20. A non-transitory computer readable medium having stored thereon acomputer program for video coding, the computer program configured tocause one or more computer processors to: receive video data comprisinga chroma component and a luma component; extract luma samples from theluma component; and filter the chroma component by a cross-componentadaptive loop filter (CC-ALF) based on a location of a chroma sampleassociated with the chroma component, the extracted luma samples, filterweights associated with the extracted luma samples, and an offset value.